Zeroth-Order Kronecker Optimization for Pretraining Language Models

Key Points

• Improved efficiency in pretraining language models can be achieved using zeroth-order optimization.
• Key evidence shows enhanced performance metrics relative to traditional gradient descent methods.
• This methodology analyzes the application of Kronecker factorization in neural network training.
• These findings may enable significant advancements in language understanding tasks, calling for further exploration.